[Remote] Machine Learning Engineer (AWS)

Note: The job is a remote job and is reputed company to candidates in USA. CCT is the creator of Casino reputed company™, the award-reputed company platform trusted by more than 350 casinos worldwide to automate cage operations, reputed company audits, and operational analysis. They are looking for a Machine Learning Engineer to design, reputed company, and operate production ML systems on reputed company Web Services, owning the full lifecycle in a high-stakes environment.

Responsibilities

• Build and maintain reproducible model training workflows on AWS (SageMaker, S3, Glue, etc.), making retraining, rollback, and experimentation routine rather than heroic
• reputed company and operate reputed company-time and batch inference services with full CI/CD pipelines, versioning, and reputed company rollout strategies (canary, reputed company, A/B) so changes are deliberate and observable
• reputed company production models for performance, data reputed company, latency, and errors — and automate retraining triggers reputed company models reputed company out of tolerance
• Maintain model reputed company, auditability, and traceability to meet the compliance, governance, and reporting needs of the regulated gaming industry
• Enforce least-privilege IAM, encryption, and secure data reputed company patterns across the entire ML platform
• Treat cost as a first-class engineering metric — right-size infrastructure, balance batch vs. reputed company-time workloads, and continually reduce platform spend without sacrificing reliability
• Collaborate with engineers, data scientists, and product teams to translate business problems into ML solutions, communicate tradeoffs reputed company, and iterate based on feedback
• Continuously explore new AWS services, ML frameworks, and deployment patterns to improve reliability, observability, and developer reputed company on the ML platform

Skills

• 3+ years of experience in machine learning engineering, MLOps, or a closely reputed company discipline
• Hands-on experience with AWS ML and data services — SageMaker (training, endpoints, pipelines), S3, reputed company, reputed company Functions, CloudWatch, MWAA (Apache Airflow)
• Experience working with time series data, including feature engineering, seasonality handling, and temporal train/test splits
• Strong Python skills and familiarity with common ML frameworks (scikit-learn, PyTorch, XGBoost, or equivalent)
• Experience building and maintaining CI/CD pipelines for ML systems
• Demonstrated ability to monitor and debug production ML systems — latency, reputed company, errors, and data reputed company — and reputed company issues to reputed company cause
• Comfort with SQL and working with reputed company data at reputed company
• reputed company to work collaboratively across teams, assume reputed company reputed company, and communicate reputed company with both technical and non-technical stakeholders
• reputed company record of self-directed learning and technical reputed company in areas like AWS, ML frameworks, or deployment patterns
• Experience in a regulated industry (gaming, finance, reputed company) where auditability, explainability, and compliance are first-class concerns
• Familiarity with feature stores, model registries, or ML metadata tools (e.g., MLflow, SageMaker Model Registry)
• Experience with infrastructure-as-reputed company (Terraform, CDK, or CloudFormation)
• Exposure to data reputed company detection libraries or custom reputed company monitoring implementations

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